87 citations · 240 across the 7 of their papers we have counts for
5 papers · 1 filter
Bridging the reality gap in quantum devices with physics-aware machine learning
D. L. Craig, H. Moon, F. Fedele +9
The discrepancies between reality and simulation impede the optimisation and scalability of solid-state quantum devices. Disorder induced by the unpredictable distribution of mater…
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
B. Severin, D. T. Lennon, L. C. Camenzind +20
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a ke…
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
V. Nguyen, S. B. Orbell, D. T. Lennon +9
Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experie…
Quantum device fine-tuning using unsupervised embedding learning
N. M. van Esbroeck, D. T. Lennon, H. Moon +8
Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of the…
Machine learning enables completely automatic tuning of a quantum device faster than human experts
H. Moon, D. T. Lennon, J. Kirkpatrick +10
Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explo…